There is an analogy between this work and the work in [1]}, where a student method is learning to mimic a teacher method. In our work the teacher method is a motion dependent one, and the student method tries to mimic the teacher during inference through motion adaptation. The teacher-student training helps the network understands the primary object in the scene in an unsupervised manner. Unlike the work in [2]} that first fine-tunes the network based on the manual segmentation mask then adapts it on-line with the most confident pixels. Our method provides a natural human robot interaction that does not require manual labelling for initialization.
